Learning fears by observing others: the neural systems of social fear transmission
Bibliographic record
Abstract
Classical fear conditioning has been used as a model paradigm to explain fear learning across species. In this paradigm, the amygdala is known to play a critical role. However, classical fear conditioning requires first-hand experience with an aversive event, which may not be how most fears are acquired in humans. It remains to be determined whether the conditioning model can be extended to indirect forms of learning more common in humans. Here we show that fear acquired indirectly through social observation, with no personal experience of the aversive event, engages similar neural mechanisms as fear conditioning. The amygdala was recruited both when subjects observed someone else being submitted to an aversive event, knowing that the same treatment awaited themselves, and when subjects were subsequently placed in an analogous situation. These findings confirm the central role of the amygdala in the acquisition and expression of observational fear learning, and validate the extension of cross-species models of fear conditioning to learning in a human sociocultural context. Our findings also provides new insights into the relationship between learning from, and empathizing with, fearful others. This study suggests that indirectly attained fears may be as powerful as fears originating from direct experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".